Cellular networks such as LTE, WiFi systems or other cellular networks face a variety of unique challenges. For example multiple factors effect throughput of cellular networks such as in a WiFi system, including but not limited to: complex interference in the Industrial, Scientific and Medical (ISM) spectrum, poor spectral efficiency of 802.11 media access control (MAC) protocol, and starvation problems associated with hidden nodes and exposed nodes.
MIMO is an important core technology for next generation wireless systems. In particular, in multi-user MIMO (MU-MIMO) systems, a base station (BS) (with M transmit antennas) communicates with multiple mobile users simultaneously using the spatial degrees of freedom at the expense of knowledge of channel state information at the transmitter (CSIT). Using simple zero-forcing precoder and near orthogonal user selection, a sum rate of M log log K can be achieved with full CSIT knowledge over K users. Yet, full CSIT knowledge is difficult to achieve in practice, and there are a lot of works focusing on reducing the feedback overhead in MIMO systems. For instance, limited-rate feedback and threshold based feedback control schemes have been proposed. A sum rate capacity O (M log log K) can be achieved when only O (M log log log K) users feeding back to the BS.
While a number of works consider reduced feedback design for MU-MIMO, these existing works focus on the throughput performance and assume infinite backlog at the base station. Therefore, the bursty arrival of data from the data source as well as the associated delay performance goes ignored, which can prove important for real-time applications. The above-described deficiencies of conventional WiFi optimization network techniques are merely intended to provide an overview of some contextual background, and are not intended to be exhaustive.